Whisper Speech Recognition MCP Server

What is Fast-Whisper-MCP-Server? Fast-Whisper-MCP-Server is a high-performance speech recognition server based on Faster Whisper, designed to provide efficient audio transcription capabilities. How to use Fast-Whisper-MCP-Server? To use the server, clone the repository, install the required dependencies, and start the server using the provided scripts. You can then configure it with compatible applications like Claude Desktop. Key features of Fast-Whisper-MCP-Server? Integrated with Faster Whisper for efficient speech recognition Batch processing acceleration for improved transcription speed Automatic CUDA acceleration if available Support for multiple model sizes (tiny to large-v3) Output formats include VTT subtitles, SRT, and JSON Model instance caching to avoid repeated loading Dynamic batch size adjustment based on GPU memory Use cases of Fast-Whisper-MCP-Server? Transcribing audio files for content creation Real-time speech recognition for applications Batch processing of multiple audio files for analysis FAQ from Fast-Whisper-MCP-Server? What are the system requirements? Requires Python 3.10+, Faster Whisper, and PyTorch with CUDA support for optimal performance. Can it handle multiple audio files at once? Yes! It supports batch transcription of audio files in a folder. Is there a GUI available? Currently, it is command-line based, but it can be integrated with GUI applications like Claude Desktop.

As an MCP (Model Context Protocol) server, Whisper Speech Recognition MCP Server enables AI agents to communicate effectively through standardized interfaces. The Model Context Protocol simplifies integration between different AI models and agent systems.

To use the server, clone the repository, install the required dependencies, and start the server using the provided scripts. You can then configure it with compatible applications like Claude Desktop. Key features of Fast-Whisper-MCP-Server? Integrated with Faster Whisper for efficient speech recognition Batch processing acceleration for improved transcription speed Automatic CUDA acceleration if available Support for multiple model sizes (tiny to large-v3) Output formats include VTT subtitles, SRT, and JSON Model instance caching to avoid repeated loading Dynamic batch size adjustment based on GPU memory Use cases of Fast-Whisper-MCP-Server? Transcribing audio files for content creation Real-time speech recognition for applications Batch processing of multiple audio files for analysis FAQ from Fast-Whisper-MCP-Server? What are the system requirements? Requires Python 3.10+, Faster Whisper, and PyTorch with CUDA support for optimal performance. Can it handle multiple audio files at once? Yes! It supports batch transcription of audio files in a folder. Is there a GUI available? Currently, it is command-line based, but it can be integrated with GUI applications like Claude Desktop.

Learn how to integrate this MCP server with your AI agents and leverage the Model Context Protocol for enhanced capabilities.